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F Face Recognition Models The human face is a highly meaningful stimulus that provides us with diverse information for adaptive social interaction with people. Our ability to recognize faces is remarkably accurate and long lasting. We are also able to categorize people along a number of visual dimensions including sex, race, and age and can readily interpret facial expression. The challenges associated with encoding and interpreting this information have become evident over the last two decades as psychol- ogists, computer scientists, and cognitive scientists have endeavored to formulate computational models of these processes. The resultant models give insight into the complexity of the problems solved by the human brain in perceiving, representing, and remem- bering faces. In this article, computational approaches to modeling the perception, categorization, and re- cognition of human faces will be presented. The properties of the human face as a visual stimulus are described first, followed by definitions of the relevant tasks we perform with faces. The steps involved in modeling these tasks are reviewed next, and rep- resentative approaches for modeling individual tasks are discussed. Finally, the article closes with a few open questions in face recognition modeling. 1. The Human Face as a Visual Stimulus The human face is a complex three-dimensional object defined by the structure of the skull and by the shape, texture, and pigmentation of the overlying skin and tissue. All faces share a basic set of features (e.g., eyes, nose, and mouth, etc.) arranged in a well-defined configuration (eyes above the nose, etc). Individual faces comprise virtually limitless variations on this standard theme. To recognize an individual from a face, we must attend to the information that makes the face unique. To categorize a face we must extract and encode the information that a face shares with an entire category of faces (e.g., male faces), but which distinguishes the category from competing categories (e.g., female faces) (see Face Recognition: Psycho- logical and Neural Aspects). 2. The Tasks ‘Face recognition’ models commonly encompass a range of tasks, including recognition, identification, verification, categorization, and the analysis of facial expression (see Facial Expressions). Face recognition refers to the judgment of whether or not a particular face is ‘known.’ Face identification refers to the retrieval of information about the ‘owner’ of the face, such as a name or context of encounter. Face veri- fication refers to a decision about whether a particular face image belongs to a particular individual. Is this person John Doe? Face verification is a common goal of face algorithms developed for security systems. 3. Modeling: A Step by Step Approach Face recognition models involve: (a) preprocessing algorithms to encode facial ‘features’ and (b) the application of this information to solve particular tasks. 3.1 Preprocessing Algorithms 3.1.1 Aligning faces. All models that involve the ana- lysis of a three-dimensional object from a two-dimen- sional image begin with the process of aligning the images into a common coordinate system. This facili- tates feature extraction and comparison. Most cur- rent face recognition models operate effectively only with frontal images, tolerating only minimal changes in viewpoint. The alignment procedure employed in different models varies both in precision and in the degree of automaticity with which it is accomplished (i.e., by hand or by a computer algorithm). At the most basic level, alignment involves image transla- tion, rotation, and scaling procedures implemented to assure that the eye levels are equivalent and that the centers of the foreheads correspond. More pre- cise alignment is possible with morphing techniques that ‘warp’ individual faces into the ‘average face’ (Craw and Cameron 1991). To morph a face into an- other face (e.g., the average face), control points are located on the two faces (usually by hand). These con- sist of facial landmarks (e.g., corners of the eyes) and supplemental points (e.g., equally spaced points along the eyebrows). Using these points as a guide, each face is warped into the shape of the average face, yielding a correspondence of the control points across all faces. This alignment enables a separable encoding of the two-dimensional shape of the face and the image intensity information. Automated solu- tions to this correspondence problem have been imple- mented using all of the pixels or surface samples of 5223

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F Face Recognition Models
The human face is a highly meaningful stimulus that provides us with diverse information for adaptive social interaction with people. Our ability to recognize faces is remarkably accurate and long lasting. We are also able to categorize people along a number of visual dimensions including sex, race, and age and can readily interpret facial expression. The challenges associated with encoding and interpreting this information have become evident over the last two decades as psychol- ogists, computer scientists, and cognitive scientists have endeavored to formulate computational models of these processes. The resultant models give insight into the complexity of the problems solved by the human brain in perceiving, representing, and remem- bering faces. In this article, computational approaches to modeling the perception, categorization, and re- cognition of human faces will be presented. The properties of the human face as a visual stimulus are described first, followed by definitions of the relevant tasks we perform with faces. The steps involved in modeling these tasks are reviewed next, and rep- resentative approaches for modeling individual tasks are discussed. Finally, the article closes with a few open questions in face recognition modeling.
1. The Human Face as a Visual Stimulus
The human face is a complex three-dimensional object defined by the structure of the skull and by the shape, texture, and pigmentation of the overlying skin and tissue. All faces share a basic set of features (e.g., eyes, nose, and mouth, etc.) arranged in a well-defined configuration (eyes above the nose, etc). Individual faces comprise virtually limitless variations on this standard theme. To recognize an individual from a face, we must attend to the information that makes the face unique. To categorize a face we must extract and encode the information that a face shares with an entire category of faces (e.g., male faces), but which distinguishes the category from competing categories (e.g., female faces) (see Face Recognition: Psycho- logical and Neural Aspects).
2. The Tasks
‘Face recognition’ models commonly encompass a range of tasks, including recognition, identification, verification, categorization, and the analysis of facial
expression (see Facial Expressions). Face recognition refers to the judgment of whether or not a particular face is ‘known.’ Face identification refers to the retrieval of information about the ‘owner’ of the face, such as a name or context of encounter. Face veri- fication refers to a decision about whether a particular face image belongs to a particular individual. Is this person John Doe? Face verification is a common goal of face algorithms developed for security systems.
3. Modeling: A Step by Step Approach
Face recognition models involve: (a) preprocessing algorithms to encode facial ‘features’ and (b) the application of this information to solve particular tasks.
3.1 Preprocessing Algorithms
3.1.1 Aligning faces. All models that involve the ana- lysis of a three-dimensional object from a two-dimen- sional image begin with the process of aligning the images into a common coordinate system. This facili- tates feature extraction and comparison. Most cur- rent face recognition models operate effectively only with frontal images, tolerating only minimal changes in viewpoint. The alignment procedure employed in different models varies both in precision and in the degree of automaticity with which it is accomplished (i.e., by hand or by a computer algorithm). At the most basic level, alignment involves image transla- tion, rotation, and scaling procedures implemented to assure that the eye levels are equivalent and that the centers of the foreheads correspond. More pre- cise alignment is possible with morphing techniques that ‘warp’ individual faces into the ‘average face’ (Craw and Cameron 1991). To morph a face into an- other face (e.g., the average face), control points are located on the two faces (usually by hand). These con- sist of facial landmarks (e.g., corners of the eyes) and supplemental points (e.g., equally spaced points along the eyebrows). Using these points as a guide, each face is warped into the shape of the average face, yielding a correspondence of the control points across all faces. This alignment enables a separable encoding of the two-dimensional shape of the face and the image intensity information. Automated solu- tions to this correspondence problem have been imple- mented using all of the pixels or surface samples of
the face rather than just a subset (Beymer and Poggio 1996, Blanz and Vetter 1999). These algorithms em- ploy elaborated optic flow computations and work well on sets of faces for which correspondence is rela- tively easy to establish, (e.g., faces without hair that are pre-aligned with the translation method). Though difficult to achieve, when successful, complete align- ment provides a powerful basis for synthesizing faces with arbitrary shapes and faces composed of inten- sity composites of other faces (Blanz and Vetter 1999).
A different approach to alignment is represented by the work of Lades et al. (1993) who developed a face recognition algorithm base on the dynamic link architecture. This algorithm combines alignment with identification. The model operates by placing a de- formable grid over the target face, sampling the face at the grid vertices. The sampling is done with a series of oriented Gabor wavelets, designed to emulate the orientation specific neurons of visual cortex. The connectors between the vertices are allowed to deform elastically, enabling a resampling of the image until the best fit is obtained. The deformation parameters of this fit serve as the face representation, which is matched to the faces in the database to identify the best match.
3.1.2 Encoding and representing faces. The informa- tion in the aligned faces must be quantified in a way that enables recognition, identification, verification, categorization, and the analysis of expression in the model. What are the features of the face? We com- monly think of the features of a face as its eyes, nose, and mouth. Descriptions of these features, such as those an eyewitness might provide, are inadequate for communicating enough information about an in- dividual face to distinguish it from competing candi- dates. Geometrical measures, e.g., distance between eyes, have proved similarly inadequate (Laughery et al. 1981). More recent models have employed rela- tively raw perceptual codes, including roughly aligned images and three-dimensional surfaces, in- cluding pigmentation information. Another code, common since the advent of morphing technology, involves a two-component separable encoding of the two-dimensional face shape and the image intensities. The ‘shape’ part of this code is defined as the defor- mation of the control points from the control points in the average face. The ‘shape-free’ part of the code consists of a ‘shape standardized’ two-dimensional array of image intensities created by warping an in- dividual face into the shape of the average face.
In current computational and psychological models of face recognition, further analysis of these perceptual codes is carried out using a principal component analysis (PCA) (Sirovich and Kirby 1987; for a review see Valentin et al. 1994). In the US Government’s tests of automatic face recognition algorithms between
1994–7, five of the seven algorithms tested used PCA. PCA is a statistical method for describing a set of correlated variables using a smaller number of uncor- related or orthogonal variables. The uncorrelated variables are called eigenvectors or principal com- ponents (PCs), denoted u
i and play the role of
‘features’ for describing the faces. PCs can be con- sidered features in the sense that any individual face, f, can be expressed as a linear combination of the PCs, Σ
i w
i u I , where the weights are the dot products, w
u I
Tf, between the faces and PCs. Because PCA is applied usually to imagessurfaces, the PCs are also imagessurfaces. Thus, individual faces can be synthe- sized as a linear combination of the PC imagessur- faces. In geometrical terms, the PCA creates a multi- dimensional space in which the PCs define the axes of the space and individual faces are points in the space. The coordinates of a face in the space are the weights that specify the face’s value on each PC feature. Note also, that three-layer back propagation networks can extract facial features similarly when they are trained to reconstruct faces through a bottleneck of hidden units. The hidden units of these auto-encoders have been shown to derive rotated versions of the PCs space (Cottrell et al. 1987).
PCA has appeal as a psychological model of face perception and memory for several reasons. First, it is consistent with psychological theories that posit a ‘face space’ metaphor for human face memory (Val- entine 1991). By this metaphor, faces can be thought of as points in a multidimensional space, with the distance between faces a measure of their similarity. At the center of the face space is the average or ‘prototype’ face. The prototype, a central concept in psychological studies of face recognition, is invoked to explain the role of face typicality in predicting recognition per- formance. Typical faces, thought to be close to the prototype, are recognized less accurately than dis- tinctive faces. This occurs presumably due to the greater density of faces close to the prototype, causing more confusion among typical faces than among distinctive faces. The prototype is also used as a reference face in creating automatic caricatures. Cari- catures can be created by ‘moving a face’ away from the average in the face space. This results in a more distinctive and recognizable version of the same face.
Second, the features that emerge from PCA are derived from the experience of the model. The role of experience in face recognition performance has been established perhaps most clearly in the phenomenon of the ‘other-race effect’—the finding that people recognize faces of their own-race more accurately than faces of other-races. This effect is predicted when the PCA is applied to a majority of faces of one race, and a smaller number of faces of other races. Because PCA derives its features from the statistical structure of the input faces, the resultant features are most appropriate for describing the majority race of faces. Conse- quently, less distinct encodings of minority race faces
Face Recognition Models
result because these faces are not well characterized by the features extracted primarily from the majority race of faces.
4. Tasks
4.1 Recognition
The quality of the stimulus representation determines the difficulty of the recognition or classification task. With a PCA-based representation, face recognition models can be implemented in a relatively simple way. A face is considered ‘known’ when an image of the individual was part of the input used to create the PCA space. The most common ‘recognition’ algorithm implements both recognition and identification. A target face is projected into the space and the distance to all other faces in the space is assessed. The nearest neighbor is chosen as the identity of the target face. Recognition can be implemented by setting a threshold distance, beyond which a target face is declared ‘unknown.’ An alternative and computationally more expedient algorithm for recognition assesses the rep- resentation error incurred by projecting the target face into the space. A threshold tolerance for error is used to determine whether a target face is known or novel.
4.2 Categorization
To categorize faces by sex, race, or age, individual exemplar faces must be assigned to different categories based on visually accessible facial features. Face categorization has been approached with supervised connectionist or neural network classifiers, such as the perceptron (seePerceptrons). Themodels use examples to learn the mapping between face representations and categories. Numerous sex categorization models have been implemented and have been found to perform at or near human performance levels. A similarly struc- tured race classifier has also been implemented, though the imbalance of experience most people have for the faces of different races must be implemented also to model human performance accurately. Finally, little work has been done on categorizing faces by age, though two complementary models of facial aging make use of morphing and caricaturing techniques, respectively. The former simulates aging by morphing individual faces toward the average of older faces (Burt and Perrett 1995). The latter simulates aging by caricaturing the three-dimensional head structure relative to a mean of young adult faces. Surprisingly, this results in an aged face (O’Toole et al. 1997).
4.3 Facial Expression Analysis
Models for categorizing faces by expression have been implemented in ways similar to sex and race classifi-
cation models, but with somewhat less success. These models operate by mapping images of faces on to expression categories using supervised learning tech- niques. Representations have varied from aligned images, to PCAs of faces preprocessed by the Gabor wavelet filters described previously. Performance has been found to be well above chance, though still short of human performance on a similar task with similar stimuli. Facial expression analysis is currently a very active area of research and more published work on this problem is expected in the near future.
5. Open Questions
Despite the clear successes of face recognition models in the 1980s and 1990s, the problem of recognizing faces from different viewpoints remains an unsolved challenge for models. Though part and parcel of the larger unsolved inverse optics problem of computer vision, the domain of faces may be more accessible due to the specific nature of face recognition as a within category problem. Some promising lines of research have begun and may soon yield new insights into this difficult problem (Edelman 1999).
See also: Linear Algebra for Neural Networks; Object Recognition: Theories; Recognition Memory, Psy- chology of; Vision, Low-level Theory of; Visual Perception, Neural Basis of
Beymer D, Poggio T 1996 Image representations for visual learning. Science 272: 1905–9
Blanz V, Vetter T 1999 A morphable model for the synthesis of 3D faces. ACM SIGGRAPH Proceedings, pp. 187–94
Burt D M, Perrett D I 1995 Perception of age in adult Caucasian male faces: Computer graphic manipulation of shape and colour information. Proceedings of the Royal Society London B Bio 259: 137–43
Cottrell G W, Munro P, Zipser D 1987 Learning internal representations of gray scale images: An example of exten- sional programming. In: Proceedings of the 9th Annual Cognitie Science Society, Erlbaum, Hillsdale, NJ
Craw I, Cameron P 1991 Parameterizing images for recognition and reconstruction. In: Mowforth P (ed.) Proceedings of the British Machine Vision Conference. Springer-Verlag, London
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Lades M, Vorbrueggen J C, Buhmann J, Lange J, von der Malsburg C, Wiskott R P, Konen R W 1993 Distortion invariant face recognition in dynamic link architectures. IEEE Transactions on Computers 42: 300–11
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Face Recognition Models
Sirovich L, Kirby M 1987 Low dimensional procedure for characterization of human faces. Journal of the Optical Society of America A 4: 519±24
Valentin D, Abdi H, O'Toole A J, Cottrell G W 1994 Connec- tionist models of face processing: A survey. Pattern Rec- ognition 27: 1209±30
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A. J. O'Toole and H. Abdi
Face Recognition: Psychological and
Neural Aspects
There is currently much debate whether `face-speci®c' neurons respond speci®cally to faces, or whether they are active when individuation of exemplars from other object categories with highly similar member items is required.
1. Behaioral Studies and Theoretical Models
Groucho Marx once said, `I never forget a face, but in your case I'll make an exception.' This statement is remarkable in that a person cannot actively choose to not recognize or remember a face. These processes proceed to completion without an apparently con- scious effort on our part, and the complexity of this operation only becomes apparent when it breaks down, e.g., when a face appears familiar, but cannot be associated with a name or context of the original interaction. What is truly remarkable is that people can recognize faces that have not been seen for long periods of time.
After birth, probably one of the ®rst objects seen repeatedly is a face. Infants actually attend more to faces than other stimulus categories (Morton and Johnson 1991). Being able to recognize the face of a parent is importantÐthe infant depends totally on them for nourishment and shelter. Research with children indicates that facial recognition develops fully by around 10 years of age (Carey 1992): at this time children no longer use a `piecemeal' approach, but begin to identify faces more `holistically,' as indicated by their impaired recognition performance when the faces are presented upside down. The inability of adults to successfully recognize inverted faces (Yin 1969) had been demonstrated previously.
Face recognition studies in adults suggest that successful face recognition proceeds in a series of stages, based on behavioral studies of normal indi- viduals and those with brain injury. This in¯uential model of face recognition (Fig. 1) was ®rst proposed by Bruce and Young in 1986. Face perception, or
detection, i.e., the ability to see a presented object as a face, and not a chair, forms the ®rst stage of this process, the so-called structural encoding stage. The face and its features are processed holistically, and the output of the structural encoder feeds directly to so- called face recognition units (FRUs). At this stage, the familiarity judgment is made. Next, the FRU output activates so-called person identity nodes (PINs), allowing the information stored on that individual (e.g., gender, age, profession, relationship to observer, usual interaction contextÐi.e., work or home, speci®c details of pleasant or unpleasant previous contact with this person, etc.) to be accessed. Finally, the output from the PINs activates the name representation for that individual. The multi-part familiar facial rec- ognition model described above can explain many errors in facial recognition that occur in everyday life (e.g., the face is familiar, but the person's name cannot be accessed), and in cases of brain injury.
Prosopagnosia is the inability to recognize pre- viously familiar faces. This condition can occur fol- lowing a stroke or as a result of a brain tumor. The individual can no longer recognize even the faces of their spouse or children. Prosopagnosia can co-occur with other visual de®cits, such as a loss of color vision (achromatopsia) and an inability to recognize every- day objects (object agnosia) (Meadows 1974). These visual de®cits co-occur in brain injury, as brain regions selectively processing color, faces, and objects are located near one another. Apperceptive proso- pagnosia is so named because the source of face recognition difficulty is largely due to disrupted basic visual perceptual mechanisms. This form of proso- pagnosia can co-occur with object agnosia, and patients often describe a degraded, fragmented visual scene. Alternatively, basic visual perception may be fairly intact, and a face is seen as a `face,' but the individual's name or their personal details cannot be accessed, i.e., associative prosopagnosia. Interestingly, the ability to recognize facial expressions is dissociable from facial recognition (Humphreys et al. 1993), prompting the idea that the brain possesses parallel pathways that deal with facial identity and facial gesture, respectively (Allison et al. 2000).
Individuals with prosopagnosia do not appear to be able to recover the ability to recognize faces once the critical areas of the brain have been damaged. Re- markably, face recognition may be `hard-wired' in the brain in the absence of postnatal experience with faces, as illustrated by a case of prosopagnosia in a 16-year- old boy who sustained his brain injury at one day of age (Farah et al. 2000). This has led researchers to hunt for specialized brain circuitry that processes faces. Additionally, recordings from single nerve cells in visually sensitive regions of monkey brains show cells that respond speci®cally to faces, and not to other object classes (for a review, see Desimone 1991, Milders and Perrett 1993). Given that humans and monkeys are both social animals, and that faces are an
Figure 1 The information-processing model of familiar face recognition as proposed by Bruce and Young (1986)
important stimulus in this context, it was thought likely that the human brain possesses nerve cells with similar response properties.
2. Neuroimaging and Neurophysiological Studies
In the latter part of the twentieth century many human physiological studies were dedicated to investigating the neural mechanisms underlying facial recognition (recently reviewed by Haxby et al. 2000). This was prompted, in part, by the development of neuro- imaging techniques such as positron emission tom- ography (PET) and, more recently, functional magnetic resonance imaging (fMRI). Both methods effectively measure focal changes in brain blood ¯ow during perception and cognition. One of the ®rst investigations of face perception and recognition was performed by Justine Sergent working at the Montreal Neurological Institute in 1992 (Sergent et al. 1992). Sergent and her colleagues performed a PET study examining differences in cerebral blood ¯ow when normal adult subjects viewed pictures of faces and discriminated between various facial attributes. For
example, subjects made gender discriminations (de- ciding whether a face was male or female), remember- ing if a particular face had been shown to the subject previously, and so on. The blood ¯ow patterns seen in these conditions were contrasted relative to conditions where subjects viewed visual material such as gratings (grids of black and white lines). These studies identi®ed regions of the occipital and temporal lobe on the underside of the brain as being selectively active when subjects viewed and discriminated between faces. Since thenmany investigators have followed suit and studied other aspects of facial processing (reviewed by Haxby et al. 2000), and the studies show concordance with this initial investigation. Additionally, it is now thought that while `face-selective' regions in both hemispheres possess the capability to process faces, it is the right hemisphere that is more important for this process. In prosopagnosia, for example, if the lesion occurs on one side of the brain it is usually on the right side (De Renzi et al. 1994).
Blood ¯ow studies show what is active in the brain; however, these methods cannot examine these changes over a ®ne time window. Recording the electrical
Figure 2 Brain regions responsive to faces as studied with electrical recordings from the surface of the human brain. (a) Schematic diagram of the underside of the human brain. Active sampled regions are shown as black circles. (b) Schematic of the side of the brain showing active regions to viewing faces. (c) Time course of electrical activity in response to the presentation of a face (denoted by vertical line). A large voltage negative (down) wave is seen at around one ®fth of a second (200 ms) after facial onset, known as an N200. (Modi®ed from Puce et al. 1999)
activity of the brain (EEG) can resolve when this activity occurs to thousandths of a second (milli- second). If the EEG is recorded from the scalp it may be difficult to identify where these active structures are in the brain. One potential way around this problem is to perform recordings of the electrical activity directly from the surface of the brain. This occurs in the routine assessment of patients who are being con- sidered for epilepsy surgery. This method has allowed the `what' and `when' of the face recognition process to be mapped accurately in both space and time. Face- selective regions of brain on the underside (Fig. 2(a)), and side of the brain (Fig. 2(b)) have been mapped using this method. Face-speci®c areas in these studies overlap those seen in neuroimaging studies in healthy subjects, and the sites of injury in prosopagnosia. After a face is presented, the brain generates a large wave (N200) at around 200 milliseconds, which is negative in voltage and is around 2¬10−% of a volt, or 200 microvolts, in size (Fig. 2(c)). The N200 event- related potential (ERP) occurs irrespective of whether the observer attempts to recognize the face or not, and does not depend on the lighting conditions, size, orientationof the face, gender, or familiarity of the face (Puce et al. 1999).
The robustness of the N200 in the large number of perceptual manipulations and the seemingly auto- matic way in which the response is generated suggests that this may be a neural correlate of the structural
encoder of Bruce and Young's (1986) model. These data are consistent with behavioral studies of face perception, where healthy subjects can readily detect faces relative to other object categories, despite stimu- lus degradation, fragmentation, rotation, inversion, manipulations of light and shade, and so on (Bruce and Young 1998).
Under these same perceptual manipulations, facial recognition can be impaired. Individuating one person's face from another requires that the features that are unique to that particular (familiar) individual are extracted and matched to a pre-existing `template.' Manipulations that impair our ability to extract subtle spatial differences will affect successful facial rec- ognition. For example, inverted familiar faces are difficult to recognize (compare Fig. 3(a) with Fig. 3(b)). Similarly, a negative image may make the face unrecognizable (Fig. 3(c)). We are forced to rely on idiosyncratic, incidental details like the cigar and moustache so that we can infer that we are looking at Groucho Marx's face in Fig. 3(c). Similarly, manip- ulations of spatial frequency content or amount of detail of the face can also impair facial recognition (Fig. 3(d), (e)).
The ability to discriminate between individual faces is based on detecting changes in subtle spatial con- ®gurations in a homogeneous object category, unlike any other object category dealt with on a daily basis. Our-specialized facial recognition skills are so honed
(a) (b) (c) (d) (e)
Figure 3 The many faces of Groucho Marx. (a) Unaltered face. (b) Inverted orientation. (c) Inverted gray-scale palette. (d) Removal of the high-spatial frequency content of the image. (e) Removal of the low-spatial frequency content
that behavioral studies have repeatedly demonstrated an own-race advantage for facial recognition across different ethnic groups, i.e., Caucasian, Asian (Brigham 1986). These data suggest that there really might be a basis for the often-heard comment from travelers that the faces of people of other races look alike. Different ethnic groups have idiosyncrasies in their facial features that an individual member of that particular group learns to differentiate. The expertise that develops with the individuals own ethnic group may hence not necessarily be generalizable to another ethnic group.
3. Does `Face-speci®c' Cortex Participate only in Face Processing?
Are faces a special stimulus category? There is no doubt that we are experts with faces. However, there is debate about the nature of this expertise, and there are currently many unanswered questions regarding these issues. For example, do `face-speci®c' regions of the brain deal only with faces, or are they also active in individuals who are experts with other object categories? i.e., are these neurons functioning speci®- cally for detecting and recognizing face, or are they a more general expert `individuator' of categories of objects with highly similar member items? (Gauthier and Logothetis 2000) Is the expertise with faces, and associated brain circuitry, that develops with the developing brain throughout childhood, different to expertise acquired with other stimulus categories in adult life?
How can these various inter-related processes be disentangled, given that we cannot test people who do not have a lifetime's exposure to faces? There are a number of approaches that are currently being under- taken in order to try and unravel these issues. First, some insight may come from studying patients with developmental prosopagnosia (Duchaine 2000). This is an extremely rare disorder, where the individual has never developed the ability to recognize faces. Physio- logical and behavioral studies of face and object recognition in these individuals relative to both healthy and brain-injured subjects may shed some
light on these questions. Second, face perception and recognition studies using cutting-edge neuroimaging techniques may be helpful. Direct recordings of electrical activity from the brain indicate that face- sensitive regions exist in a patchy mosaic with regions responsive to objects, and to words, for example. The relatively coarse spatial resolution in most neuro- imaging studies to date could produce blood-¯ow measures containing contributions from different kinds of category-speci®c regions, making it difficult to evaluate exactly how these brain regions deal with facial information. Studies of facial recognition per- formed with (high ®eld strength) functional MRI combined with recordings of the electrical activity of the brain in the same subject may ®nally shed some light on why it is impossible to forget a face, despite our best attempts to do so.
See also: Face Recognition Models; Facial Expres- sions; Neural Representations of Objects; Object Recognition: Theories; Prosopagnosia; Visual Per- ception, Neural Basis of
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Face Recognition: Psychological and Neural Aspects
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A. Puce
Facial Expressions
1. Introduction
This article describes one aspect of human communi- cation and behavior—facial expressions. It describes the characteristics of a facial expression and its biological wiring, and then offers a selective history of research on facial expressions that emphasizes the nature vs. nurture debate over the origins of facial expressions of emotions. The article concludes with some current research issues and future directions of facial expression research.
2. Defining Facial Expressions
Of all the forms of human communication, which includes the written and spoken word, body language, and so forth, facial expressions are recognized as among the most salient and influential. Researchers reserve the term ‘facial expression’ for those recurring configurations of facial muscle movements that com- municate some thought, emotion, or behavior. This is because not all recurring facial muscle configurations express specific messages. For example, some facial muscle actions that accompany spoken words—such
as raising one’s eyebrows when emphasizing a par- ticular word—may modify those words, but are not messages in and of themselves (e.g., Ekman 1991).
The face can express various thoughts. For example, a person who raises the outer corner of one eyebrow may convey sophisticated skepticism. A person whose eyebrows are pulled up in the middle may convey sympathy for another. A wink can convey that one is kidding. Flashing both eyebrows upward may convey a greeting. Or, lowered eyebrows may convey un- certainty (Eibl-Eibesfeldt 1989). Researchers agree for the most part that these types of facial expressions are learned like language, displayed under conscious control, and their meanings are culturally specific that rely on context for proper interpretation (e.g., Bird- whistell 1970). Thus, the same lowered eyebrow expression that would convey ‘uncertainty’ in North America might convey ‘no’ in Borneo (Darwin 18721998).
The face can also express emotions. For example, humans express the emotion of happiness by raising lip corners into what is commonly called a smile. Humans can express sadness by frowning. Besides happiness and sadness, other emotions that seem to have specific facial expressions include anger, disgust, fear, and surprise, and to a lesser extent contempt, embarrassment, interest, pain, and shame (e.g., Ekman 1993, Izard 1991). What makes the facial expression of these aforementioned emotions different from other facial expressions is that there is evidence that these emotions are expressed and interpreted the same across all cultures (e.g., Ekman 1993, Izard 1971). This ‘universal’ production and perception across cultures suggests that those emotions and their specific facial expressions might be determined geneti- cally, rather than socially learned. However, this claim is not without controversy (e.g., Russell 1994).
3. Neuroanatomy of Facial Expression
The idea that facial expressions can be both de- termined genetically, as in the case of some of the emotions, and learned socially, as in the case of all other facial expressions, is supported by an exam- ination the neuroanatomy of the face. There appears to be two distinct neural pathways that mediate facial expressions, each originating in a different area of the brain; one area for the voluntary, willful facial actions (the cortical motor strip), and the second area for the more involuntary, emotional facial actions (subcorti- cal areas; reviewed by Rinn 1984). This dual origin hypothesis is supported by clinical observations of patients who are paralyzed on one side of their face. When these patients were asked to pose a smile, they could only smile on half their face. Yet when these same patients felt the spontaneous emotion of en- joyment after being told a funny joke, they were able to smile on both sides of their face. Likewise, patients with lesions of the subcortical areas of the brain such
Face Recognition: Psychological and Neural Aspects
as the basal ganglia have difficulty showing spon- taneous, emotional facial expressions; however, these patients are able to move their facial muscles on command. These facial action observations are so reliable that they serve as diagnostic criteria for brain lesions.
4. History of Facial Expression Research
The turbulent history of the systematic study of facial expressions began with the publication of Darwin’s book The Expression of the Emotions in Man and Animals (18721998). In this book, Darwin proposed that humans across all cultures have particular and distinct facial expressions for particular emotions, and that these expressions are produced involuntarily as a result of that emotion. Darwin defined emotions as behavioral and physiological reactions that have helped humans and animals to survive the various life challenges they faced throughout their evolutionary history. For example, the fear reaction assisted humans and animals escape danger, the anger reaction assisted humans and animals to fight rivals, and so forth. Those who possessed these emotional reactions were more likely to live to reproductive age and therefore pass their genes to the next generation (see Emotions, Eolution of).
What Darwin argued (and elaborated by others, e.g., Ekman 1991, Izard 1991, Plutchik 1991) is that social animals, such as humans, must communicate these emotions to others in the group because emo- tions express imminent behavior, such as striking out in anger, fleeing in fear, and so on (see Emotions, Psychological Structure of ). The facial expression of anger thus becomes the visual signal of this intention to strike. This signal allows others in the group to avoid this person, and thus avoid a potential fight (although others argue that these expressions would deprive an individual of a competitive advantage like the element of surprise prior to an attack, e.g., Fridlund 1994). These facial expressions of emotion were seen as vestiges of an entirely nonverbal human communication system that must have existed in extinct human forms such as Neanderthal, because only modern humans have the throat structures necessary to produce articulate speech. A further clue to this prehistoric human communication comes from the genetically closest living relatives of humans, the chimpanzees, who have a repertoire of facial expres- sions of emotion that parallel, but are not identical to, human facial expressions of emotion. Thus, current human facial expression of emotion represents more the communication methods of the past genetic history of the species, rather than its present conditions (Brown 1991).
However, much of the empirical work that followed Darwin’s book failed to support his notion that there were particular facial expressions for particular emo-
tions. For example, when subjects were startled by firecrackers, embarrassed, or disgusted by having to decapitate a live rat, their most common facial expression shown across all these situations was a smile, even though these subjects were not experi- encing positive emotion. Likewise, observations of people outside North America by social scientists cast further doubt; for example, the smile was observed as an expression of uncertainty in Africans but as an expression of sadness in Japanese women (see Culture and Emotion). Findings such as these—although there were scattered findings to the contrary—caused scholars to conclude that facial expressions did not provide accurate information as to emotional state (Birdwhistell 1970). Thus, by the early 1960s, social science seemed to conclude that all facial expres- sions—including facial expressions of emotion—were culturally relative, socially learned, and that there were no universals.
Despite this conclusion, two theorists revived Darwin’s ideas about the evolutionary origins of facial expressions of emotion (Plutchik 1991, Tomkins 19621963). These researchers took photographs of people posing prototypical emotions such as anger, disgust, fear, happiness, and so on, and found that observers would agree as to which expression repre- sented which emotion. Other researchers found similar results with various European, South American, African, and Asian cultures (e.g., Izard 1971). Pro- ponents of the social learningcultural relativism perspective counter-argued that the populations upon which this evidence for universality was based were mostly educated, and thus could have learned from various forms of media which expressions represented which emotions (e.g., Birdwhistell 1970). To parry this argument, researchers conducted similar studies with visually isolated peoples whom had limited contact with Westerners, and thus could not have learned these expressions from the media (e.g., the Sadong of Borneo, and Fore in New Guinea). These researchers found for the most part the same pattern of universal expression and recognition of facial expressions of emotion as in the Westernized peoples (e.g., Ekman 1993). Follow up research using a variety of methodo- logical alterations to this basic paradigm found pat- terns consistent universality throughout the 1970s and 1980s (e.g., Izard 1991). Finally, parallel evidence in favor of universality came from observations of children who were born blind and deaf, and who could not have seen these facial expressions to learn how to express them. These children showed similar expres- sions of emotion as their sighted counterparts (Eibl- Eibesfeldt 1989).
However, proponents of Darwin’s idea were still stuck with the findings that peoples of different cultures sometimes showed different expressions for a given emotion than North Americans. Ekman pro- posed that the reason this happened was that different cultures learned different rules to regulate their facial
Facial Expressions
expression of emotion—what he called ‘display rules’ (Ekman 1993). For example, Japanese culture has a display rule that prohibits expression of anger or disgust to higher status people, unlikeNorth American culture. Researchers found that both groups showed facial expressions of disgust when viewing a gruesome film alone. But when in the presence of a high status person, the Japanese group hid their disgust feelings with a smile, whereas the Americans still showed disgust expressions. This concept of display rules seemed to account for why people smiled to such seemingly different events as the death of a loved one, confusion, uncertainty, startle, sexual excitement, disgust, and so on (Ekman 1991). Based on these findings, Ekman (1993) proposed his neurocultural theory of emotions. This theory argued that certain basic human emotions generated particular patterns of physiology and facial expressions, that these facial expressions were universal across all cultures, but that their ultimate expression was modified, exacerbated, suppressed, or masked by social learning processes dependent upon personal, family, or cultural display rules (see Adulthood: Emotional Deelopment ).
5. Current Facial Expression Research
By the early 1990s, a consensus seemingly emerged in the field of psychology that Darwin was correct after all—that some facial expressions of emotion were universal. This was not a peaceful consensus; social scientists who placed the uniqueness of culture at the forefront of any understanding of emotion were not convinced of universality, based on the observations described earlier (e.g., Russell 1994). Experimental psychology itself issued two challenges to universality in the early 1990s. One challenge suggested that all facial expressions were simply communicative ges- tures, that is, they are not the result of internal emotional states, but only the result of the social motives of the person within a particular context (the ‘behavioral ecology’ view; Fridlund 1994). The beha- vioral ecology view found that facial expression, particularly smiling, was related not to felt emotion, but to the presence of others. Proponents of uni- versality counter-argued that not all smiles are the same. They demonstrated that only one type of smile, called the enjoyment smile, is related to the positive emotional experience of enjoyment, as measured through self-report or pattern of brain activity. This enjoyment smile looks different from other smiles in that only enjoyment smiles feature orbicularis oculi action (the muscles surrounding the eye that give a ‘crow’s feet’ appearance) along with zygomatic major action (the typical lip corner raising). Failing to note the distinction between enjoyment and other smiles may have been why other researchers found no relationship between smiling and positive emotion (Ekman and Rosenberg 1998).
A second challenge to universality attacked the concepts behind what was meant by universality, as well as the methods used to document universality. These methodological problems—such as biased re- sponse forms and preselected facial expressions— when added together may have conspired to bias observers’ judgments, causing them to artificially agree on which facial expression represented which emotion (Russell 1994). Prompted by these criticisms, ensuing experimental research corrected many of the proposed methodological shortcomings, and has so far recon- firmed support for the universality of facial expres- sions of emotion (Ekman 1994).
Although the issue of the biological vs. social origins of facial expressions of emotion is not fully resolved, what has been impressive is the amount of current research generated by the findings on universality of facial expressions of emotion (Ekman 1993). First, researchers have shown that people who pose and hold these universal facial expressions of emotion begin to experience the particular emotion they are posing— although researchers have debated the exact role of a facial expression in reflecting vs. causing an emotion (e.g., Buck 1988). Regardless, this phenomenon has enabled researchers to document physiologically spec- ific patterns of arousal for specific emotions, and in more than one culture. Second, these universal facial expressions of emotion have been employed in studies of brain activity, leading researchers to discover that there are centers in the human brain that respond specifically to these expressions (e.g., the amygdala responds to fear expressions; Whalen 1998). Third, this work on the universal facial expressions has prompted researchers to examine their origins in children (e.g., Izard and Malatesta 1987). This work has shown that children as young as 12 months of age react differently to their mothers’ expressions of fear versus to happiness; a mother’s fear expression will stop a child’s risky behavior, whereas a mother’s happy expression will not (see Emotions, Children’s Understanding of ).
These universal expressions of emotion have also shown utility as markers of social and psychological functioning. For example, the presence of enjoyment smiles on the part of a person who has survived the death of their romantic partner predicts successful coping with that traumatic loss. Schizophrenic patients tend to show different, and sometimes fewer or more disorganized facial expressions than normal patients (reviewed by Ekman and Rosenberg 1998). Mothers show different sorts of smiles to their difficult compared to their nondifficult children. The facial expression of disgust or contempt, but not anger, predicts marital divorce (Gottman 1994). Researchers found that these unbidden facial expressions of emo- tion can occur for very brief flashes, called ‘micro- expressions,’ that under certain circumstances can betray deception (Ekman 1991). Thus, current re- search on facial expressions has moved away from
Facial Expressions
documenting the existence of these emotional facial expressions and has moved toward examining the implications of the presence or absence of these facial expressions and their corresponding emotions on human social development, interaction, relationships, and psychopathology.
6. Future Directions of Facial Expression Research
Advances in technology will aid facial expression research by allowing researchers to quickly, validly, and reliably observe facial expressions. This will be helpful to the field because current work on facial expression is extremely time and labor intensive, or suffers from other experimental concerns. For ex- ample, visible scoring systems, that require close examination of videotape, can take 60 minutes to analyze one minute of behavior (e.g., Ekman and Rosenberg 1998, Izard and Malatesta 1987). Electro- myographic techniques, that use electrodes on the face to measure the faint electrical impulses produced by muscle contractions, suffer from concerns about the salience of electrodes on a person’s face affecting the behavior of that person (e.g., Fridlund 1994). In the future, computer based analysis programs will be developed to assess the specific muscle movements associated with facial expressions at a much faster, and more reliable way, than these older methods, without causing awareness on the part of the person being analyzed. This will have the effect of making research on facial expression more accessible to more researchers, which can only help the field progress more quickly than in the past.
There are many questions ‘facing’ future facial expression research, and space limitations permit only a description of a few. First, researchers will try to clarify the stimuli and processes by which social information elicits an emotion and its expression (the ‘appraisal’ process; Scherer et al. 2000), as well as the process by which people learn to control their emo- tional facial expressions. This inquiry might provide researchers with a gateway to understanding better the role of expression in the experience and management of emotions. It will also lead to understanding how perceptions of facial expressions may account for differences in social competence and functioning, or ‘emotional intelligence’ of adults and children. An offshoot of this work would explore whether profes- sionals and lay people can be trained to improve their accuracy at interpreting emotional expressions, and the implications this has for their relationships. Se- cond, researchers will move toward investigating more interactional research designs that place the facial expression of emotion back into the social context which it is typically embedded, to measure the conse- quences of such expression in the real world. Third, given that much of the previous work has been with
posed expressions, future work would employ more spontaneous facial expressions. Fourth, with the assistance of technology that allows noninvasive ob- servation of the working brain (e.g., Positron Emission Tomography or functional Magnetic Resonance Imaging), researchers will continue to use facial expressions of emotion to map where in the brain the expressions are perceived as well as where they are generated (e.g., Whalen 1998). Fifth, an examination of these first four future directions will inevitably lead to a better understanding of individual differences in production, control, and recognition of facial expres- sions, of which there is little work at present. Finally, this work would need to be expanded to cultures other than Europe or North America to assess the relative universality of the process of emotion, its antecedents, attempts to control, and its effect on facial expression (e.g., Ekman 1993).
Research on facial expressions has both paralleled and driven changes in the general assumptions in the field of psychology. The finding that people of all cultures seemed to agree on which facial expressions represented which emotions pushed psychology to- ward re-examining the biological bases for behavior. But research on facial expressions will continue to be controversial because it exposes the strong feelings of those who believe in the power of social situations to mold all human behavior, expressive or not, and those who believe in the biological origins of some of those behaviors. Thus, debates over facial expressions are really debates about human nature—a debate that has tormented social science from time immemorial. What research on facial expression has done is to help move this debate away from an argument over political beliefs about human nature, and toward an argument over observable data.
See also: Adulthood: Emotional Development; Cult- ure and Emotion; Emotion and Expression; Emotion: History of the Concept; Emotion in Cognition; Emo- tion,NeuralBasisof;Emotional InhibitionandHealth; Emotions and Health; Emotions, Children’s Under- standing of; Emotions, Evolution of; Emotions, History of; Emotions, Psychological Structure of; Emotions, Sociology of; Face Recognition Models; Face Recognition: Psychological and Neural Aspects; Infancy and Childhood: Emotional Development; Psychological Therapies: Emotional Processing
Birdwhistell R L 1970 Kinesics and Context. University of Pennsylvania Press, Philadelphia, PA
Brown D E 1991 Human Uniersals. McGraw-Hill, New York Buck R 1988 Human Motiation and Emotion. Wiley, New York Darwin C 18721998 The Expression of the Emotions in Man and
Animals. Oxford University Press, New York Eibl-Eibesfeldt I 1989 Human Ethology. de Gruyter, New York
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Ekman P 1991 Telling Lies. Norton, New York Ekman P 1993 Facial expression and emotion. American
Psychologist 48: 384–92 Ekman P 1994 Strong evidence for universals in facial expres-
sions: A reply to Russell’s mistaken critique. Psychological Bulletin 115: 268–87
Ekman P, Rosenberg E L (eds.) 1998 What the Face Reeals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, New York
Fridlund A J 1994 Human Facial Expression: An Eolutionary View. Academic Press, San Diego
Gottman J 1994 Why Marriages Succeed or Fail. Simon & Schuster, New York
Izard C E 1971 The Face of Emotion. Appleton-Century Crofts, New York
Izard C E 1991 Human Emotions. Plenum Press, New York Izard C E, Malatesta C Z 1987 Perspectives on emotional
development. I. Differential emotions theory of early emo- tional development. In: Osofsky J D (ed.) Handbook of Infant Deelopment, 2nd edn. Wiley, New York, pp. 494–554
Plutchik R 1991 The Emotions: Facts, Theories, and a New Model. University Press, New York
Rinn W E 1984 The neuropsychology of facial expression: A review of the neurological and psychological mechanisms for producing facial expressions. Psychological Bulletin 95: 52–77
Russell J A 1994 Is there universal recognition of emotion from facial expression? A review of cross-cultural studies. Psycho- logical Bulletin 115: 102–41
Scherer K R, Schorr A, Johnstone T 2000 (eds.) Appraisal Processes in Emotion: Theory, Methods, Research. Oxford University Press, New York
Tomkins S S 19621963 Affect, Imagery, Consciousness (Vol. 1, The Positie Affects, Vol. 2, The Negatie Affects). Springer, New York
Whalen P J 1998 Fear, vigilance, and ambiguity: Initial neuro- imaging studies of the human amygdala. Current Directions in Psychological Science 7: 177–88
M. G. Frank
Faction: Political
In its broadest construction, a political faction is any part of a political whole. The term has been applied to phenomena ranging from sets of people whose policy preferences tend to align to membership groups that undertake collective action.
In a most famous formulation Madison (Hamilton et al. 1961) defined a faction as a partisan political division of any size, although he qualified the defini- tion to refer to such groups whose aims were ‘adverse to the rights of other citizens, or to the permanent and aggregate interests of the community.’ Madison viewed factions as the inevitable consequence of political liberty and social diversity, and urged the ‘extended sphere’ of a federal republic to contain ‘the violence of faction.’ Madison’s treatment of the ‘mischief of faction’ is often given as evidence for
the American founders’ distaste for such common- place features of representative democracy as political parties and lobbying groups.
For current analytical purposes, Madison’s defini- tion of faction is overly broad, in encompassing political parties and interest groups, and overly re- strictive in parsing motives. A good, if still broad, definition is that a political faction is a subsidiary part of a political institution, of a political party, a lobbying organization, or a legislature. Unlike political parties within a polity or within a legislature, factions com- monly have no legal standing in the institution of which they are part, although they might play an important and enduring role in organizing it. Sub- groups that have legal status within institutions are usually designated in other ways, as are the caucuses in the United States Congress and the international political parties in the International Typographical Union. Sometimes analysts identify as factions group- ings that are evanescent and happenstantial, for instance a voting coalition on a particular legislative bill or party nomination. Most, however, limit the term to groupings that are enduring and widely recognized. The factional affiliations of Liberal Demo- crats standing for election to the Japanese Diet, for instance, were routinely reported in the newspapers. The factions within the Democratic Party in West Virginia, while not reported in the press, nevertheless persisted through changes in political administration and party leadership.
In any complex society, a tendency toward faction within political parties and interest groups would seem to be inevitable. Members of parties and groups might be united on the central purposes of the organization but still divided on secondary questions. For decades, the Democratic Party in the United States was loosely united on issues of economic policy but deeply divided into northern and southern factions on issues of race and federal powers, to the point where party allegiances were often superseded by a conservative coalition, an informal alliance of Republicans and southern Democrats, in the US Congress. A common purpose in clericalism still left the Christian Demo- crats, Italy’s confessional party, vulnerable to factions arising from region and class.
Although they are more difficult to observe, similar factional divisions occur in many lobbying groups. At several points in its history, the American Farm Bureau Federation harbored two or three identifiable factions that reflected regional commodity interests. In the 1920s, the Anti-saloon League foundered on a factional division over the question of whether the League ought to emphasize the enforcement of Pro- hibition in the United States or the promotion of the cause of prohibition abroad.
Considerable evidence exists, however, that some political parties and some interest groups are more prone to faction than others. Political parties in Japan, Italy, and France in the Fourth Republic were
Facial Expressions
famously factionalized, while parties in Norway, Sweden, and the German Federal Republic were not.
Several conditions appear to promote a tendency toward factional strife in political parties. One factor is electoral domination: factionalization appears more likely in parties that face no real electoral challenge. The Italian Christian Democrats (DC) and the Japanese Liberal Democrats (LDP) are striking both for their enduring factional divisions and their long runs of electoral success—the DC participated in every Italian government from World War II into the 1990s, and the LDP controlled the Japanese Diet continu- ously from 1955 to 1993. In the American southern states, the electoral threat posed by the mountain Republicans produced a Democratic Party in North Carolina and Tennessee that was appreciably more cohesive than the Democratic Party in states like South Carolina, Florida, Mississippi, and Arkansas, where the Republican Party constituted barely even a nuisance. While counterexamples of dominant parties not rent by faction abound—the Social Democrats in Sweden, the Republicans in Vermont—a considerable body of theory, beginning with the work of Riker (1962) in fact predicts that parties that enjoy more than minimal majorities will be prone to factional disintegration (see Minimum Winning Coalition, in Politics).
A second influence on the propensity toward factionalization is the structure of political cleavage within the society (Lipset and Rokkan 1967). In Belgium, class divisions cut across parties that are organized primarily on the basis of language and religion. In Italy, economic and regional differences fueled factions within a confessional party whose raison d’etre is the establishment of the Roman Catholic Church. In Sweden, by contrast, religious homogeneity and a dominant secularism caused re- ligious disputes to recede well behind the class divisions that are the basis for the political parties (see Cleaages: Political).
A third influence on faction is the electoral system. The electoral laws place an upper bound on the number of political parties that can be sustained, a limit equal to one more than the number of candidates to be elected from the constituency (Cox 1997). Accordingly, the electoral laws govern the ease with which factional divisions can progress to outright schism and the formation of new political parties. In the Low Countries of Belgium and the Netherlands, highly proportional representation rules coupled with low election thresholds have produced party systems in which parties and factions morph each into the other with remarkable fluidity. Japan’s system of multi-member districts with a single nontransferable vote fostered a half-dozen factions that slated candi- dates and oversaw the distribution of spoils. The hard limit of a system of single-member districts and plurality voting in the United States forced northern and southern Democrats to stick together, despite
their factional animosities. In the United States, the advent of nominating primaries—creating, effectively, single-member constituencies with plurality voting within parties—often limited the number of enduring party factions to two, as in North Dakota on the Republican side or in Louisiana on the Democratic side. In North Dakota, in fact, the more liberal wing of the Republican Party, the Nonpartisan League, split off and joined the Democrats in the 1950s, when two- party competition finally became viable.
The ease or difficulty of schism also contributes to the propensity of interest groups to be ridden with factions. As Hirschman (1970) put it, when exit is costly, members resort to voice. Interest groups that rely primarily on the expressive value of the group’s purposes to motivate members seem especially prone to faction (Wilson 1974). Doctrinal purity matters when members are motivated primarily by doctrine. The National Organization for Women, for example, suffered through disputes between a faction that wished to pursue conventional lobbying for the Equal Rights Amendment and a faction that wished to promote social movement activities like direct action. Interest groups that rely upon expressive benefits, moreover, tend also to be prone to schism, because ‘purposive’ inducements to group involvement are so easy to provide. In the later part of the twentieth century, the ‘public interest groups’ in the United States multiplied like Protestant denominations, rapidly and schismatically. A dissident faction of the Sierra Club, for example, bolted the organization to form Friends of the Earth; a few years later, a dissident faction of Friends of the Earth created the Envi- ronmental Policy Center. In contrast, interest groups that attractmemberswith relatively expensive material benefits like insurance, or interest groups that are able to secure membership through coercion, make it costly for dissidents to exit, channeling what might have been schism into faction. Factional fights within labor unions typically continue intramurally rather than extramurally; the costs a new organization would have to bear to win the right to represent workers in collective bargaining preclude exit. The American Medical Association (AMA) has endured despite tensions between specialists and general practitioners, between doctors in large practices and in small practices, between academics and clinicians, bound by the range of AMA services and by the state powers that have been delegated to the medical societies to regulate medical employment.
The potential for faction in interest groups has long been seen as an important limit on the power and influence of lobbying groups in American politics. Pluralist political scientists identified ‘overlapping memberships,’ that is, conflicting internal interests, as an important limit on the demands that interest groups might make. During the energy policy debates of the 1970s, for instance, the National Petroleum Refiners Association took no position on crude oil price
Faction: Political
regulation, immobilized by conflicts between the small independent refiners and the ‘majors.’ Likewise, the potential for internal disagreement sidelined many of the large business associations, like the US Chamber of Commerce and the National Association of Manu- facturers, in the debates over the extension of the Reciprocal Trade Agreements Act in the 1950s. ‘Quasiunanimity,’ Bauer et al. (1972) concluded, was a ‘premise of action’ in lobbying groups. A group that was too extreme in its demands risked disabling internal controversy.
In political parties, factions emerge and recombine to produce change in party systems, as has occurred recently in both Japan and Italy. But factional divisions also hobble parties in the achievement of their policy goals. The most dramatic example is surely the decades-long obstruction of civil rights legislation, a Democratic Party priority since the Truman administration, by the Party’s southern Dixiecrat faction. But if factionalism is joined with limited party competition, many argue, the con- sequences are the more severe. When parties are isolated from the judgment of the electorate, factions dispute not policy direction but the division of the spoils. The extreme factionalization of the Liberal Democratic Party in Japan and the Christian Demo- cratic Party in Italy contributed to the inefficiency, particularism, and corruption of those two dominant parties. But at least the Japanese and Italian factions were relatively enduring and organized. As Key (1950) argued of the Democratic Party in the American southern states, where commonly it was ‘every man for himself ’ in elections, competition between ephem- eral factions within a hegemonic party is no substitute for competition between two or more political parties. Political parties develop a stake in their policy reputa- tions that factions commonly do not. Popular control in a democracy requires the clear electoral choices that political parties provide and party factions cannot.
See also: Cleavages: Political; Electoral Systems; Fac- tionalism; Interest Groups; Interest Groups, History of; Interest: History of the Concept; Lobbying; Mini- mum Winning Coalition, in Politics; Party Systems; Political Parties
Bauer R A, Pool I deS, Dexter L A 1972 American Business and Public Policy. Aldine-Atherton, New York
Cox G W 1997 Making Votes Count. Cambridge University Press, New York
Hamilton A, Madison J, Jay J 1961 In: Rossiter C (ed.) The Federalist Papers. New American Library, New York, no. 10
Hirschman A O 1970 Exit, Voice, and Loyalty. Harvard Uni- versity Press, Cambridge, MA
Key V O Jr 1950 Southern Politics. Knopf, New York Laver M, Schofield N 1990 Multiparty Goernment. Oxford
University Press, New York Lipset S M, Rokkan S 1967 Cleavage structures, party systems,
and voter alignments. In: Lipset S M, Rokkan S (eds.) Party Systems and Voter Alignments. Free Press, New York
Mayhew D R 1986 Placing Parties in American Politics. Princeton University Press, Princeton, NJ
Ramseyer J M, Rosenbluth F McC 1993 Japan’s Political Marketplace. Harvard University Press, Cambridge, MA
Riker W 1962 The Theory of Political Coalitions. Yale University Press, New Haven, CT
Wilson J Q 1974 Political Organizations. Basic Books, New York
J. M. Hansen
Factionalism refers to dissension between rival sub- groups—factions—within a larger social unit. Fac- tionalism can take many forms and has been observed in all parts of the world. It is a basic political process dynamically related to social change.
1. Factions and Organizations
Factionalism, regardless of where it is classified on the conflict continuum, is conflict between factions. Fac- tions are coalitions of persons or subgroups that compete over specific issues within a larger organiz- ation or community. The central focus of a faction is the leader who coordinates its activities and recruits its members. Ties between leader and followers are usually personal, although some followers may recruit others on behalf of the leader. The issues about which factions compete are diverse. They generally concern scarce resources the control over which provide power chances, such as economic assets, office and new laws. But they may also involve honor, ideology and behavioral norms. Loosely structured, factions are non-corporate groups that generally dissolve when the particular issues that gave rise to them are resolved. But if the issues remain unresolved, factions may acquire a range of cultural trappings such as property, symbols, ideology and bureaucratic organization. They can then evolve into permanent corporate groups, such as ritual moieties, political parties or other formal associations, for which the term faction is no longer appropriate.
Factionalism takes place within the framework of an established social entity, whether village, school, political party, office, club, kin group, etc., that have clear norms of behaviour. These norms generally include notions of unity, consensus, and cooperation.
Faction: Political
The covert maneuvering of factions bent on achieving their own, often self-serving aims, contradict these norms. Hence factionalism has a pejorative conno- tation. It is seen as subverting organizational rules and goals. Faction members are consequently viewed as disloyal, obstructive persons whose pursuit of narrow, short-term advantage endangers the wider, long-term goals of the organization. The divisiveness inherent in factionalism also hampers the day to day affairs of the organization or community that depend on coop- eration. Furthermore, factionalism jeopardizes the good name and image of unity and harmony it seeks to project to the outside world.
2. The Study of Factionalism
The study of factions and factionalism developed slowly, shadowing theoretical shifts in the social sciences. Although Linton (1936) long ago suggested that factions presented an interesting but unexplored field, little was done until the 1950s. Political anthro- pology until then was dominated by the functionalist paradigm elaborated in African Political Systems (Fortes and Evans-Pritchard 1940). This viewed poli- tics as maintaining order through consensus, har- mony, and balanced opposition. The political groups on which functionalists focused were enduring units, corporate groups. Conflict, if examined, was viewed as reinforcing the social structure. Loosely structured, temporary coalitions such as factions patently did fit into this conception of politics. The theoretical he- gemony of Africanist political anthropologists began to be challenged in the 1950s. Not surprisingly, Raymond Firth, given his interest in individual choice, had for some time been uncomfortable with the functionalist paradigm. He and colleagues, who had observed factionalism in Indian communities, were the first to examine factionalism theoretically (Firth 1957). They treated factions as informal counterparts of more formal political formations whose members were recruited according to structurally diverse princi- ples. They also noted that factions tended to become activated on specific occasions and not as regularly recurring features. Other studies of factions and factionalism swiftly followed (Siegel and Beals 1960, Boissevain 1964, 1974, Nicholas 1965, Bailey 1969, Thoden van Velzen 1973, Bujra 1973, Alavi 1973). Most employed a transactionalist perspective, viewing political activity as an arena in which entrepreneurs transact personal relations for political and economic gain. The study of factionalism culminated in the late 1970s with the volume edited by Silverman and Salisbury (1977). This is still considered the definitive work on the subject. Following its publication and the demise of functionalist social science—in which trans- actionalists played a significant role—academic debate moved on to puzzles related to symbolic, cognitive, and discursive approaches to politics. Fac- tions and factionalism have become accepted concepts
whose characteristics are no longer debated. They are also proving useful to related disciplines (see Brumfiel and Fox 1994).
3. Issues
Much of the work on factionalism in the 1960s, although using a transactionalist perspective, con- tinued to be strongly influenced by functionalism. Factionalism was seen as occurring as a result of rapid sociocultural change. Change was seen as coming from the surrounding environment, not as the result of tensions inherent in the community or society in which factionalism arose. Factionalism was generally viewed as occurring because the system’s equilibrium was disturbed, and it operated to restore its dynamic equilibrium. Factions were regarded as structurally similar and in balanced opposition. These viewpoints were debated and successfully challenged during the 1970s.
Many of the societies in which factionalism was observed were in fact not subject to rapid social change. But if factionalism was not always a ‘result’ of change, it seemed always to be ‘about’ change: changes in power resources, ideology, rules andor ways of doing things. Factions competed about who was to be boss, about which normative concepts were to be followed, about whose will was to prevail, and thus about which rules were to be followed. Moreover, a closer examination of rival factions revealed that far from being similar or evenly matched, they differed in their access to resources, strategy, tactics, internal organization, ideology, and social composition.
4. Factions: Structure, Symmetry, and Balance
Factions often form in opposition to or in defense of some issue or some pre-existing source of power and authority within a community or organization. The distribution of such resources is binary. Some persons have more and some have less. Those with more normally constitute the local establishment that coalesces around a leader or a dominant personage such as a headman, mayor, parish priest, or club president. They and their supporters form the ‘es- tablishment’ faction. Those who are dissatisfied with the establishment’s exercise of its power constitute a category from which a rival or ‘opposition’ faction can be recruited.
Reports also mention conservative and progressive factions. Because the local establishment normally defends the ‘status quo,’ from which it derives its superordinate position, it is often labeled as con- servative. Since the opposition faction challenges the established defenders of the ‘status quo’ it is labelled progressive. These labels often do not reflect reality, as when a progressive opposition faction defeats its rival and becomes the dominant, establishment faction.
There is a further reason why opposition factions come to be regarded as progressive. Since they are weaker, they are perhaps more receptive to new resources if and when these become available in the wider society. With these they can challenge their rivals. In rapidly changing societies these may include new government and commercial offices, new laws and new ideologies. Such new resources rapidly tend to change the balance of power. Factionalism thus does not necessarily result from the availability of new resources, as some authors have suggested. Rather, new resources are used in ongoing competitions for power and prestige and tend to escalate the conflict. The use of new resources is not random. A faction will use new resources when it seems likely that they will strengthen its position. It is then labelled progressive, in the sense of favoring change of the ‘status quo.’
There is evidence that conflict groups—whether faction, ritual moiety, or political party—differ organ- izationally. When the opposition consists merely of a category of persons disgruntled with the dominant power elite, they are obviously less organized than the establishment. The internal structure of the local establishment faction, whose members are used to networking to maintain their position, will normally have a more developed exchange circuit than the opposition faction. But if a conflict between the two persists over time, the opposition faction may well become better organized than its rival may. Good organization is a valuable resource and one of the ways a weaker faction can successfully challenge its rival. It is thus more open than the establishment faction to organizational innovation. Because of its superior resources, the dominant faction also tends to be more wasteful. It does not need to husband its resources to the extent that its weaker rival does.
Like most coalitions, factions have core and per- ipheral members. Core members cluster around the leader and have multiple links to each other. The peripheral members are often linked only to the leader or to a single member of the core group. Where there is a strong core, the faction often acquires some of the characteristics of corporate groups noted previously.
Factions are not necessarily ideologically neutral, as some authors have suggested. The differences between establishment and opposition, like those between conservative and progressive, are not random. They have ideological implications. There is evidence that opposition factions recruit more support than their rivals from weaker or even marginal social categories do. Since opposition leaders usually lack the network ties and material resources that establishment leaders use to recruit followers, they cannot afford to be too particular about the nature of their support. The strength of a faction is usually a function of its size. Just as they often turn to new ideologies and tactics— which because they are new are often viewed as socially unacceptable and subversive—opposition leaders also recruit supporters from among those who are less
influential or are regarded as social or morally inferior. Followers are followers.
Opposition factions consequently often develop or adopt an overarching ideology or symbol to bind their heterogeneous members into a unity. They also often align themselves with political parties that defend the interests of their socially weaker supporters. Most often, opposition factions seek links to parties that embody an emancipatory ideology. Their establish- ment rivals develop relations with political parties representing vested interests in the wider society. Political parties, on the other hand, also consciously make use of local factions to recruit support at the grass roots level.
5. Conclusion
If factionalism is not necessarily a product of social change, it appears to be always about change. Factions are coalitions that compete for power to determine, and thus to change, what is to be accepted as normal. Rival factions, because they have different access to power chances, are not evenly matched, structurally similar groups. Their asymmetry is fundamental to understanding the nature of factionalism and its dynamic for long term change.
Structural asymmetry and competition for power are also characteristic of class-based conflict groups. This suggests that the line of cleavage between faction, class or party cuts across moral categories and socio- economic classes, not at right angles, as most func- tionalist and class-oriented analysts postulate, but diagonally. Where the line approaches the vertical, forming conflicting coalitions with a generally even spread across socio-economic classes, it is reasonable to speak of factionalism, in the case of face-to-face groups, and party conflict in the case of conflict on a broader scale. Where the line of cleavage approaches the horizontal, forming conflict groups that are more clearly differentiated according to socio-economic criteria, the term class conflict seems appropriate. But in every case the axis of cleavage must be determined by empirical investigation. It should not be taken for granted.
See also: Charisma: Social Aspects of; Conflict and Conflict Resolution, Social Psychology of; Conflict: Anthropological Aspects; ConflictConsensus; Con- flict: Organizational; Conflict Sociology; Faction: Political; Groups, Sociology of; Issue Networks: Iron Triangles, Subgovernments, and Policy Communities; Leadership, Psychology of; Solidarity, Sociology of
Alavi H 1973 Peasant classes and primordial loyalties. Journal of Peasant Studies. 1: 232–62
Bailey F G 1957 Caste and the Economic Frontier. Manchester University Press, Manchester, UK
Bailey F G 1969 Stratagems and Spoils. Basil Blackwell, Oxford, UK
Boissevain J 1964 Factions, parties and politics in a Maltese village. American Anthropologist. 66: 1275–87
Boissevain J 1974 Friends of Friends: Networks, Manipulators and Coalitions. Basil Blackwell, Oxford, UK
Brumfiel E M, Fox J W (eds.) 1994 Factional Competition and Political Deelopment in the New World. Cambridge Uni- versity Press, Cambridge, UK
Bujra J 1973 The dynamics of political action: A new look at factionalism. American Anthropologist. 75: 132–52
Firth R 1957 Introduction to factions in Indian and overseas Indian societies. British Journal of Sociology. 8: 291–5
Fortes M, Evans-Pritchard E E (eds.) 1940 African Political Systems. Oxford University Press, London
Linton R 1936 The Study of Man. D. Appleton-Century, New York
Nicholas R W 1965 Factions: A comparative analysis. In: Banton M (ed.) Political Systems and the Distribution of Power. Tavistock, London
Siegel B, Beals A R 1960 Pervasive factionalism. American Anthropologist. 62: 395–417
Silverman M, Salisbury R F (eds.) 1977 A House Diided? Anthropological Studies of Factionalism. Memorial University, Newfoundland, Canada
Thoden van Velzen H U E 1973 Coalitions and network analysis. In: Boissevain J, Mitchell J C (eds.) Network Analysis: Studies in Human Interaction. Mouton, The Hague, The Netherlands
J. Boissevain
Confirmatory factor analysis (CFA) is a quantitative data analysis method that belongs to the family of structural equation modeling (SEM) techniques. CFA allows for the assessment of fit between observed data and an a priori conceptualized, theoretically grounded model that specifies the hypothesized causal relations between latent factors and their observed indi- cator variables. Because population-level equivalence between data and model cannot be proven with sample data, CFA should be viewed as a mainly disconfirmatory technique. That is, CFA facilitates the statistical rejection—or, at best, a very tentative retention—of a specific theory regarding the factor(s) responsible for the observed relations in the data. If, on the other hand, the investigator’s intentions are a mostly ungrounded exploration of relations suggested by the data, classical exploratory factor analysis is the more appropriate approach. In this entry, typical steps in a CFA are introduced theoretic-
ally and via example: from model specification and identification, to parameter estimation, data-model fit assessment, and potential model modification. Applied and methodological references are provided for a more in-depth study of CFAand SEM techniques in the social and behavioral sciences.
1. Oeriew
The term ‘factor analysis’ describes a host of methods, all of which have the purpose of facilitating a better understanding of the latent, unobserved variables (factors) that underlie a set of directly measurable and observed variables. These factors are often believed to represent constructs, psychological or otherwise, that have a direct bearing on the measured variables; as such they are assumed to motivate (and in turn be inferable from) the pattern of correlations or covari- ances among those observed variables. In the late 1960s, works by Karl Jo reskog (e.g., 1966, 1967) articulated a method for confirmatory factor analysis (CFA), an application of normal theory maximum likelihood estimation to factor models with specific theoretical latent structures. Such structures could include the a priori specification of the number of factors, their orthogonality or obliquity, and which variables had zero and nonzero relations with those factors. This distinguishes CFA from well-known exploratory factor analysis (e.g., Gorsuch 1983, Mulaik 1972) wherein the number and nature of the factors emerge from the observed variables’ data through a mathematical algorithm, largely blind to any substantive theory. Most crucial in Jo reskog’s CFA work was the provision for a formal statistical χ-test of the fit between the pattern of relations among themeasured variables and the theorized factor model, thereby facilitating the disconfirmation or tentative confirmation of an hypothesized factor model. Soon after, Jo reskog and others put forth a more general framework for the integration of measured and latent variables into complex causal networks, serving as the foundation for